{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<div style=\"text-align: center\"><font color='#dc2624' face='微软雅黑' size = \"6\">RFM 分析</font></div>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" # 代码块显示所有执行结果"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<div style=\"text-align: left\"><font color='black' face='微软雅黑' size = \"6\">引言</font><a name='top'></a></div>**\n",
    "\n",
    "RFM是市场分析的一种业务模型， 用于评估1个用户的价值，对众多用户进行分级，帮助制订营销策略，其由3个指标组成：<br/>\n",
    "* **Recency**, 最近一次消费时间。\n",
    "* **Frequency**, 消费频率, 指一段时间范围内的消费次数。不同类别商品的消费频率差异很大，有些商品属于耗材，因此消费频率不适合跨品类比较。\n",
    "* **Monetary Value**, 消费金额，指一段时间范围内的消费总金额。\n",
    "通过RFM模型的3个指标，至少可以将用户分为4种类型：best, loyal, risk, lost。当然根据业务需求的不同，由很多种划分规则。\n",
    "![image-2.png](attachment:image-2.png)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#需要的包\" data-toc-modified-id=\"需要的包-1\">需要的包</a></span></li><li><span><a href=\"#数据清洗\" data-toc-modified-id=\"数据清洗-2\">数据清洗</a></span></li><li><span><a href=\"#计算RFM指标\" data-toc-modified-id=\"计算RFM指标-3\">计算RFM指标</a></span></li><li><span><a href=\"#EDA\" data-toc-modified-id=\"EDA-4\">EDA</a></span></li><li><span><a href=\"#聚类\" data-toc-modified-id=\"聚类-5\">聚类</a></span></li><li><span><a href=\"#划分用户等级\" data-toc-modified-id=\"划分用户等级-6\">划分用户等级</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 需要的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div id=\"bm6OnI\"></div>\n",
       "            <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
       "                if(!window.letsPlotCallQueue) {\n",
       "                    window.letsPlotCallQueue = [];\n",
       "                }; \n",
       "                window.letsPlotCall = function(f) {\n",
       "                    window.letsPlotCallQueue.push(f);\n",
       "                };\n",
       "                (function() {\n",
       "                    var script = document.createElement(\"script\");\n",
       "                    script.type = \"text/javascript\";\n",
       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v2.2.0/js-package/distr/lets-plot.min.js\";\n",
       "                    script.onload = function() {\n",
       "                        window.letsPlotCall = function(f) {f();};\n",
       "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        \n",
       "                    };\n",
       "                    script.onerror = function(event) {\n",
       "                        window.letsPlotCall = function(f) {};    // noop\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        var div = document.createElement(\"div\");\n",
       "                        div.style.color = 'darkred';\n",
       "                        div.textContent = 'Error loading Lets-Plot JS';\n",
       "                        document.getElementById(\"bm6OnI\").appendChild(div);\n",
       "                    };\n",
       "                    var e = document.getElementById(\"bm6OnI\");\n",
       "                    e.appendChild(script);\n",
       "                })()\n",
       "            </script>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据处理\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 可视化\n",
    "from lets_plot import LetsPlot, ggplot, aes, geom_bar, ggsize, coord_flip\n",
    "LetsPlot.setup_html() # 默认开启JS交互模式\n",
    "# LetsPlot.setup_html(no_js=True) # 关闭JS交互模式\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Pie\n",
    "from palettable.colorbrewer.colorbrewer import get_map\n",
    "\n",
    "# 聚类\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗\n",
    "数据来源: https://www.kaggle.com/carrie1/ecommerce-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('./data.csv',encoding='unicode_escape')\n",
    "df1['InvoiceDate'] = pd.to_datetime(df1['InvoiceDate'], format='%m/%d/%Y %H:%M')\n",
    "df1.shape # 54W行\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo              object\n",
       "StockCode              object\n",
       "Description            object\n",
       "Quantity                int64\n",
       "InvoiceDate    datetime64[ns]\n",
       "UnitPrice             float64\n",
       "CustomerID            float64\n",
       "Country                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      False\n",
       "StockCode      False\n",
       "Description     True\n",
       "Quantity       False\n",
       "InvoiceDate    False\n",
       "UnitPrice      False\n",
       "CustomerID      True\n",
       "Country        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "InvoiceNo       0.000000\n",
       "StockCode       0.000000\n",
       "Description     0.268311\n",
       "Quantity        0.000000\n",
       "InvoiceDate     0.000000\n",
       "UnitPrice       0.000000\n",
       "CustomerID     24.926694\n",
       "Country         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查缺失值\n",
    "df1.isna().any(axis=0)\n",
    "df1.isna().sum(axis=0) # 缺失值数量\n",
    "df1.isna().agg(lambda x: x.sum(axis=0)/x.shape[0] * 100) # 缺失值比例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "字段'CustomerID'缺失值比例搞到24.9%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2010-12-01 08:26:00')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-09 12:50:00')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计时间范围\n",
    "df1['InvoiceDate'].min()\n",
    "df1['InvoiceDate'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice\n",
       "count  541909.000000  541909.000000\n",
       "mean        9.552250       4.611114\n",
       "std       218.081158      96.759853\n",
       "min    -80995.000000  -11062.060000\n",
       "25%         1.000000       1.250000\n",
       "50%         3.000000       2.080000\n",
       "75%        10.000000       4.130000\n",
       "max     80995.000000   38970.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>freq</th>\n",
       "      <th>perct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>495478</td>\n",
       "      <td>91.431956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>9495</td>\n",
       "      <td>1.752139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>8557</td>\n",
       "      <td>1.579047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>8196</td>\n",
       "      <td>1.512431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2533</td>\n",
       "      <td>0.467422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2371</td>\n",
       "      <td>0.437527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2069</td>\n",
       "      <td>0.381798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.369435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>1519</td>\n",
       "      <td>0.280305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1259</td>\n",
       "      <td>0.232327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country    freq      perct\n",
       "0  United Kingdom  495478  91.431956\n",
       "1         Germany    9495   1.752139\n",
       "2          France    8557   1.579047\n",
       "3            EIRE    8196   1.512431\n",
       "4           Spain    2533   0.467422\n",
       "5     Netherlands    2371   0.437527\n",
       "6         Belgium    2069   0.381798\n",
       "7     Switzerland    2002   0.369435\n",
       "8        Portugal    1519   0.280305\n",
       "9       Australia    1259   0.232327"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计数字变量分布\n",
    "df1[['Quantity', 'UnitPrice']].describe()\n",
    "\n",
    "# 统计离散变量频率\n",
    "for col in ['Country']:\n",
    "    (df1[col].value_counts()\n",
    "     .reset_index()\n",
    "     .set_axis([col, 'freq'], axis=1)\n",
    "     .sort_values(by='freq', ascending=False)\n",
    "     .eval(\"perct = freq / freq.sum()*100\", engine='python')\n",
    "     .iloc[0:10, :]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 既然我们是对用户进行分级，那么用户ID为缺失值的就没有意义了，过滤掉即可\n",
    "* 字段'Quantity'和'UnitPrice'为负数可能表示退货，这里并不需要分析退货，同样过滤掉。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(397884, 8)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55       17850  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39       17850  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75       17850  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39       17850  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39       17850  United Kingdom  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = (df1.query(\"CustomerID.notna()\", engine='python')\n",
    "       .query(\"(Quantity > 0) & (UnitPrice > 0)\")\n",
    "       .eval(\"CustomerID = CustomerID.astype('int')\")\n",
    "      )\n",
    "df2.shape # 还剩39W行\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算RFM指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4219, 4)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>14646</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>271614.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4086</th>\n",
       "      <td>18102</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>231822.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3636</th>\n",
       "      <td>17450</td>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "      <td>192521.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>16446</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>168472.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>14911</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "      <td>136087.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  recency  frequency   monetary\n",
       "1653       14646        1         71  271614.14\n",
       "4086       18102        0         56  231822.69\n",
       "3636       17450        8         44  192521.95\n",
       "2936       16446        0          2  168472.50\n",
       "1840       14911        1        188  136087.12"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df2 = (df2.query(\"InvoiceDate.dt.year >= 2011\", engine='python') # 筛选时间范围\n",
    "           .eval(\"total = Quantity * UnitPrice\")\n",
    "           .eval(\"InvoiceDate = InvoiceDate.dt.normalize()\", engine='python') # 提取日期\n",
    "           .set_index('CustomerID')\n",
    "           .groupby(level='CustomerID')\n",
    "           .agg(\n",
    "               recency = ('InvoiceDate', max), \n",
    "               frequency = ('InvoiceNo', lambda x: x.nunique()),\n",
    "               monetary = ('total', sum)\n",
    "           )\n",
    "           .reset_index()\n",
    "           .pipe(lambda df: df.assign(recency = df['recency'].max() - df['recency'])) # timedelta\n",
    "           .eval(\"recency = recency.dt.days\", engine='python')\n",
    "           .sort_values(by=['monetary', 'recency', 'frequency'], ascending=[False, True, False])\n",
    "           \n",
    "          )\n",
    "rfm_df2.shape\n",
    "rfm_df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "看一下变量分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(x=\"monetary\", y=\"recency\", data=rfm_df2, height=7)\n",
    "g.ax_joint.set_xscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(x=\"monetary\", y=\"frequency\", data=rfm_df2, height=7)\n",
    "g.ax_joint.set_xscale('log')\n",
    "g.ax_joint.set_yscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(x=\"frequency\", y=\"recency\", data=rfm_df2, height=7)\n",
    "g.ax_joint.set_xscale('log');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**小结:**<br/>\n",
    "* 大部分用户recency在100天以内，50天以内占很大比例。\n",
    "* 大部分用户toal monetary在1000以内。\n",
    "* 大部分用户frequency在3次以内，1次的占很大比例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average silhouette_score is : 0.6119463712168937\n"
     ]
    }
   ],
   "source": [
    "# 特征工程\n",
    "X = StandardScaler().fit_transform(rfm_df2[['monetary', 'recency', 'frequency']].values)\n",
    "\n",
    "# 建模\n",
    "n_clusters = 4\n",
    "clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "cluster_labels = clusterer.fit_predict(X)\n",
    "rfm_result = rfm_df2.assign(label=cluster_labels) # 聚类结果\n",
    "\n",
    "# 评价指标\n",
    "silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "silhouette_values = silhouette_samples(X, cluster_labels)\n",
    "print('The average silhouette_score is :', silhouette_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`\"Silhouette\"`方法是用来解释和验证样本与簇之间的关联程度，不同的簇，用不同的颜色表示，横坐标为Silhouette系数，越接近1表示该簇与其它簇之间的边界越清晰，越接近0表示该簇与其它簇的边界越模糊，如果为负数则表示很可能误分类。竖线为平均Silhouette系数，越大表示聚类边界越清晰。[参考](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html)<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6), dpi=100)\n",
    "fig.set_size_inches(18, 7)\n",
    "ax.set_xlim([-0.1, 1])\n",
    "y_lower = 10\n",
    "for i in range(n_clusters):\n",
    "    ith_cluster_silhouette_values = silhouette_values[cluster_labels == i]\n",
    "    ith_cluster_silhouette_values.sort()\n",
    "    size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "    y_upper = y_lower + size_cluster_i\n",
    "    color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "    ax.fill_betweenx(\n",
    "        np.arange(y_lower, y_upper),\n",
    "        0,\n",
    "        ith_cluster_silhouette_values,\n",
    "        facecolor=color,\n",
    "        edgecolor=color,\n",
    "        alpha=0.7,\n",
    "    )\n",
    "    ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "    y_lower = y_upper + 10  \n",
    "ax.set_title(\"The silhouette plot for the various clusters.\")\n",
    "ax.set_xlabel(\"The silhouette coefficient values\")\n",
    "ax.set_ylabel(\"Cluster label\")\n",
    "ax.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "ax.set_yticks([]) # 隐藏刻度和标签\n",
    "ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "筛选聚类异常的标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3677</th>\n",
       "      <td>17511</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>84351.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2636</th>\n",
       "      <td>16029</td>\n",
       "      <td>38</td>\n",
       "      <td>57</td>\n",
       "      <td>67912.32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>14096</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>65164.79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3097</th>\n",
       "      <td>16684</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>63540.16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  recency  frequency  monetary  label\n",
       "3677       17511        2         28  84351.30      1\n",
       "0          12346      325          1  77183.60      1\n",
       "2636       16029       38         57  67912.32      1\n",
       "1261       14096        4         17  65164.79      1\n",
       "3097       16684        4         24  63540.16      1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>14646</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>271614.14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4086</th>\n",
       "      <td>18102</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>231822.69</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3636</th>\n",
       "      <td>17450</td>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "      <td>192521.95</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>16446</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>168472.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>14911</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "      <td>136087.12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  recency  frequency   monetary  label\n",
       "1653       14646        1         71  271614.14      3\n",
       "4086       18102        0         56  231822.69      3\n",
       "3636       17450        8         44  192521.95      3\n",
       "2936       16446        0          2  168472.50      3\n",
       "1840       14911        1        188  136087.12      3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_result.query(\"label == 1\").head()\n",
    "rfm_result.query(\"label == 3\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4219.000000</td>\n",
       "      <td>4219.000000</td>\n",
       "      <td>4219.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1976.462198</td>\n",
       "      <td>84.346765</td>\n",
       "      <td>4.060678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8670.578595</td>\n",
       "      <td>90.080697</td>\n",
       "      <td>7.101487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>304.360000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>664.850000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1614.610000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>271614.140000</td>\n",
       "      <td>339.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            monetary      recency    frequency\n",
       "count    4219.000000  4219.000000  4219.000000\n",
       "mean     1976.462198    84.346765     4.060678\n",
       "std      8670.578595    90.080697     7.101487\n",
       "min         3.750000     0.000000     1.000000\n",
       "25%       304.360000    17.000000     1.000000\n",
       "50%       664.850000    47.000000     2.000000\n",
       "75%      1614.610000   128.000000     4.000000\n",
       "max    271614.140000   339.000000   188.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df2[['monetary', 'recency', 'frequency']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与右侧的分位数对比，不难发现: <br/>\n",
    "* 'label == 3'对应的用户群，frequency比较大，recency比较小，同时monetary非常大，属于高价值客户。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 划分用户等级\n",
    "&emsp;&emsp;通过分箱划分用户等级, 如下表所示就是一种根据具体业务划分用户的标准。因为我们不了解具体的业务需求，所以我们这里无法根据业务需要来划分用户等级。因此采用通常的分位数分箱。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table class=\"table table\">\n",
    "<thead><tr>\n",
    "<th style=\"text-align:left;\">\n",
    "Segment\n",
    "</th>\n",
    "<th style=\"text-align:left;\">\n",
    "Description\n",
    "</th>\n",
    "<th style=\"text-align:left;\">\n",
    "R\n",
    "</th>\n",
    "<th style=\"text-align:left;\">\n",
    "F\n",
    "</th>\n",
    "<th style=\"text-align:left;\">\n",
    "M\n",
    "</th>\n",
    "</tr></thead>\n",
    "<tbody>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "Champions\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Bought recently, buy often and spend the most\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "4 - 5\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "4 - 5\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "4 - 5\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "Loyal Customers\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Spend good money. Responsive to promotions\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "2 - 5\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "3 - 5\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "3 - 5\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "Potential Loyalist\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Recent customers, spent good amount, bought more than once\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "3 - 5\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "1 - 3\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "1 - 3\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "New Customers\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Bought more recently, but not often\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "4 - 5\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 1\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 1\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "Promising\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Recent shoppers, but haven’t spent much\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "3 - 4\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 1\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 1\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "Need Attention\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Above average recency, frequency &amp; monetary values\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "2 - 3\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "2 - 3\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "2 - 3\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "About To Sleep\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Below average recency, frequency &amp; monetary values\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "2 - 3\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 2\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 2\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "At Risk\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Spent big money, purchased often but long time ago\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 2\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "2 - 5\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "2 - 5\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "Can’t Lose Them\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Made big purchases and often, but long time ago\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 1\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "4 - 5\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "4 - 5\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "Hibernating\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Low spenders, low frequency, purchased long time ago\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "1 - 2\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "1 - 2\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "1 - 2\n",
    "</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td style=\"text-align:left;\">\n",
    "Lost\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "Lowest recency, frequency &amp; monetary scores\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 2\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 2\n",
    "</td>\n",
    "<td style=\"text-align:left;\">\n",
    "&lt;= 2\n",
    "</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义1个函数，对1个指标进行分箱\n",
    "def add_segments_by_cut(x, nodes, labels, right=True): # x is a series\n",
    "    x = pd.Series(x.values)\n",
    "    if len(nodes) + 1 != len(labels):\n",
    "        raise ValueError(\"length of 'labels' must be equal to length of 'nodes' plus 1\")\n",
    "    segments = pd.Series(np.ones(x.__len__())).astype('int').astype('string')\n",
    "    if right:\n",
    "        segments.mask(x <= nodes[0], labels[0], inplace=True)\n",
    "        for i in range(nodes.__len__() - 1):\n",
    "            segments.mask((x > nodes[i]) & (x <= nodes[i+1]), labels[i+1], inplace=True) # right默认左开右闭\n",
    "        segments.mask(x > nodes[-1], labels[-1], inplace=True)\n",
    "    else:\n",
    "        segments.mask(x < nodes[0], labels[0], inplace=True)\n",
    "        for i in range(nodes.__len__() - 1):\n",
    "            segments.mask((x >= nodes[i]) & (x < nodes[i+1]), labels[i+1], inplace=True) # 左闭右开\n",
    "        segments.mask(x >= nodes[-1], labels[-1], inplace=True)\n",
    "    return segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果是其它划分节点，修改参数即可。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "quartile_R = rfm_df2['recency'].quantile([0.25, 0.5, 0.75]).to_list()\n",
    "quartile_F = rfm_df2['frequency'].quantile([0.25, 0.5, 0.75]).to_list()\n",
    "quartile_M = rfm_df2['monetary'].quantile([0.25, 0.5, 0.75]).to_list()\n",
    "\n",
    "rfm_df3 = rfm_df2.copy()\n",
    "rfm_df3['R_group'] = add_segments_by_cut(rfm_df3['recency'], quartile_R, ['≤ Q1', '(Q1, Q2]', '(Q2, Q3]', '≤ Q4'])\n",
    "rfm_df3['F_group'] = add_segments_by_cut(rfm_df3['frequency'], quartile_F, ['≤ Q1', '(Q1, Q2]', '(Q2, Q3]', '≤ Q4'])\n",
    "rfm_df3['M_group'] = add_segments_by_cut(rfm_df3['monetary'], quartile_M, ['≤ Q1', '(Q1, Q2]', '(Q2, Q3]', '≤ Q4'])\n",
    "rfm_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>R_group</th>\n",
       "      <th>F_group</th>\n",
       "      <th>M_group</th>\n",
       "      <th>rfm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>14646</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>271614.14</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>中中中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4086</th>\n",
       "      <td>18102</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>231822.69</td>\n",
       "      <td>近</td>\n",
       "      <td>低</td>\n",
       "      <td>低</td>\n",
       "      <td>近低低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3636</th>\n",
       "      <td>17450</td>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "      <td>192521.95</td>\n",
       "      <td>远</td>\n",
       "      <td>低</td>\n",
       "      <td>低</td>\n",
       "      <td>远低低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>16446</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>168472.50</td>\n",
       "      <td>远</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>远中中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>14911</td>\n",
       "      <td>1</td>\n",
       "      <td>188</td>\n",
       "      <td>136087.12</td>\n",
       "      <td>远</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>远中中</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  recency  frequency   monetary R_group F_group M_group  rfm\n",
       "1653       14646        1         71  271614.14       中       中       中  中中中\n",
       "4086       18102        0         56  231822.69       近       低       低  近低低\n",
       "3636       17450        8         44  192521.95       远       低       低  远低低\n",
       "2936       16446        0          2  168472.50       远       中       中  远中中\n",
       "1840       14911        1        188  136087.12       远       中       中  远中中"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartile_R = rfm_df2['recency'].quantile([0.25, 0.5, 0.75]).to_list()\n",
    "quartile_F = rfm_df2['frequency'].quantile([0.25, 0.5, 0.75]).to_list()\n",
    "quartile_M = rfm_df2['monetary'].quantile([0.25, 0.5, 0.75]).to_list()\n",
    "\n",
    "rfm_df3 = rfm_df2.copy()\n",
    "rfm_df3['R_group'] = add_segments_by_cut(rfm_df3['recency'], quartile_R, ['近', '中', '中', '远'])\n",
    "rfm_df3['F_group'] = add_segments_by_cut(rfm_df3['frequency'], quartile_F, ['低', '中', '中', '高'])\n",
    "rfm_df3['M_group'] = add_segments_by_cut(rfm_df3['monetary'], quartile_M, ['低', '中', '中', '高'])\n",
    "rfm_df3['rfm'] = rfm_df3['R_group'] + rfm_df3['F_group'] + rfm_df3['M_group']\n",
    "rfm_df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "cell_style": "center"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rfm</th>\n",
       "      <th>freq</th>\n",
       "      <th>perct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中中中</td>\n",
       "      <td>727</td>\n",
       "      <td>17.231571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>近高高</td>\n",
       "      <td>475</td>\n",
       "      <td>11.258592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>远低低</td>\n",
       "      <td>423</td>\n",
       "      <td>10.026073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中低低</td>\n",
       "      <td>350</td>\n",
       "      <td>8.295805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中低中</td>\n",
       "      <td>311</td>\n",
       "      <td>7.371415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>中高高</td>\n",
       "      <td>301</td>\n",
       "      <td>7.134392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>近中中</td>\n",
       "      <td>295</td>\n",
       "      <td>6.992178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>远低中</td>\n",
       "      <td>274</td>\n",
       "      <td>6.494430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>远中中</td>\n",
       "      <td>218</td>\n",
       "      <td>5.167101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>中中高</td>\n",
       "      <td>146</td>\n",
       "      <td>3.460536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>中高中</td>\n",
       "      <td>121</td>\n",
       "      <td>2.867978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>近高中</td>\n",
       "      <td>113</td>\n",
       "      <td>2.678360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>中中低</td>\n",
       "      <td>95</td>\n",
       "      <td>2.251718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>近低低</td>\n",
       "      <td>72</td>\n",
       "      <td>1.706566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>远中低</td>\n",
       "      <td>71</td>\n",
       "      <td>1.682863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>近中高</td>\n",
       "      <td>64</td>\n",
       "      <td>1.516947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>近中低</td>\n",
       "      <td>41</td>\n",
       "      <td>0.971794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>近低中</td>\n",
       "      <td>40</td>\n",
       "      <td>0.948092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>远中高</td>\n",
       "      <td>28</td>\n",
       "      <td>0.663664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>中低高</td>\n",
       "      <td>18</td>\n",
       "      <td>0.426641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>远低高</td>\n",
       "      <td>13</td>\n",
       "      <td>0.308130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>远高中</td>\n",
       "      <td>10</td>\n",
       "      <td>0.237023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>远高高</td>\n",
       "      <td>9</td>\n",
       "      <td>0.213321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>近高低</td>\n",
       "      <td>2</td>\n",
       "      <td>0.047405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>远高低</td>\n",
       "      <td>1</td>\n",
       "      <td>0.023702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>近低高</td>\n",
       "      <td>1</td>\n",
       "      <td>0.023702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rfm  freq      perct\n",
       "0   中中中   727  17.231571\n",
       "1   近高高   475  11.258592\n",
       "2   远低低   423  10.026073\n",
       "3   中低低   350   8.295805\n",
       "4   中低中   311   7.371415\n",
       "5   中高高   301   7.134392\n",
       "6   近中中   295   6.992178\n",
       "7   远低中   274   6.494430\n",
       "8   远中中   218   5.167101\n",
       "9   中中高   146   3.460536\n",
       "10  中高中   121   2.867978\n",
       "11  近高中   113   2.678360\n",
       "12  中中低    95   2.251718\n",
       "13  近低低    72   1.706566\n",
       "14  远中低    71   1.682863\n",
       "15  近中高    64   1.516947\n",
       "16  近中低    41   0.971794\n",
       "17  近低中    40   0.948092\n",
       "18  远中高    28   0.663664\n",
       "19  中低高    18   0.426641\n",
       "20  远低高    13   0.308130\n",
       "21  远高中    10   0.237023\n",
       "22  远高高     9   0.213321\n",
       "23  近高低     2   0.047405\n",
       "24  远高低     1   0.023702\n",
       "25  近低高     1   0.023702"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算用户等级的频率和占比\n",
    "(rfm_df3['rfm'].value_counts()\n",
    " .reset_index()\n",
    " .set_axis(['rfm', 'freq'], axis=1)\n",
    " .eval(\"freq = freq.astype('float')\", engine='python')\n",
    " .eval(\"perct = freq/freq.sum()*100\", engine='python')\n",
    " .eval(\"freq = freq.astype('int')\", engine='python')\n",
    " .sort_values(by='freq', ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['中中中', '中中低', '中中高', '中低中', '中低低', '中低高', '中高中', '中高高', '近中中', '近中低', '近中高', '近低中', '近低低', '近低高', '近高中', '近高低', '近高高', '远中中', '远中低', '远中高', '远低中', '远低低', '远低高', '远高中', '远高低', '远高高']\n"
     ]
    }
   ],
   "source": [
    "print(rfm_df3['rfm'].drop_duplicates().sort_values().to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rfm</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>R_group</th>\n",
       "      <th>F_group</th>\n",
       "      <th>M_group</th>\n",
       "      <th>segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中中中</td>\n",
       "      <td>14646</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>271614.14</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>普通用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>中中中</td>\n",
       "      <td>15311</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>58091.24</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>普通用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中中中</td>\n",
       "      <td>15749</td>\n",
       "      <td>235</td>\n",
       "      <td>3</td>\n",
       "      <td>44534.30</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>普通用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中中中</td>\n",
       "      <td>15838</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>32685.58</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>普通用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中中中</td>\n",
       "      <td>15039</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>18596.68</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>中</td>\n",
       "      <td>普通用户</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rfm  CustomerID  recency  frequency   monetary R_group F_group M_group  \\\n",
       "0  中中中       14646        1         71  271614.14       中       中       中   \n",
       "1  中中中       15311        0         82   58091.24       中       中       中   \n",
       "2  中中中       15749      235          3   44534.30       中       中       中   \n",
       "3  中中中       15838       11         18   32685.58       中       中       中   \n",
       "4  中中中       15039        9         45   18596.68       中       中       中   \n",
       "\n",
       "  segment  \n",
       "0    普通用户  \n",
       "1    普通用户  \n",
       "2    普通用户  \n",
       "3    普通用户  \n",
       "4    普通用户  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "segment_df = pd.DataFrame(\n",
    "    {\n",
    "        'rfm':     ['中中中', '中中低', '中中高', '中低中', '中低低', \n",
    "                    '中低高', '中高中', '中高高', '近中中', '近中低', \n",
    "                    '近中高', '近低中', '近低低', '近低高', '近高中', \n",
    "                    '近高低', '近高高', '远中中', '远中低', '远中高', \n",
    "                    '远低中', '远低低', '远低高', '远高中', '远高低', '远高高'],\n",
    "        'segment': ['普通用户', '低价值', '较高价值', '较低价值', '低价值', \n",
    "                    '易流失', '潜力用户', '较高价值', '普通用户', '低价值', \n",
    "                    '潜力用户', '新用户', '低价值', '潜力用户', '普通用户', \n",
    "                    '低价值', '高价值', '已流失', '已流失', '已流失', \n",
    "                    '已流失', '已流失', '已流失', '已流失', '已流失', '已流失']\n",
    "    }\n",
    ").set_index('rfm')\n",
    "\n",
    "rfm_df4 = rfm_df3.set_index('rfm').join(segment_df, how='left').reset_index()\n",
    "rfm_df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"VsYWYX\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"rfm\",\n",
       "\"fill\":\"segment\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"coord\":{\n",
       "\"name\":\"flip\",\n",
       "\"flip\":true\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":800,\n",
       "\"height\":400\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..count..\":[727.0,295.0,113.0,95.0,350.0,41.0,72.0,2.0,146.0,301.0,311.0,18.0,121.0,64.0,1.0,40.0,475.0,218.0,71.0,28.0,274.0,423.0,13.0,10.0,1.0,9.0],\n",
       "\"rfm\":[\"中中中\",\"近中中\",\"近高中\",\"中中低\",\"中低低\",\"近中低\",\"近低低\",\"近高低\",\"中中高\",\"中高高\",\"中低中\",\"中低高\",\"中高中\",\"近中高\",\"近低高\",\"近低中\",\"近高高\",\"远中中\",\"远中低\",\"远中高\",\"远低中\",\"远低低\",\"远低高\",\"远高中\",\"远高低\",\"远高高\"],\n",
       "\"segment\":[\"普通用户\",\"普通用户\",\"普通用户\",\"低价值\",\"低价值\",\"低价值\",\"低价值\",\"低价值\",\"较高价值\",\"较高价值\",\"较低价值\",\"易流失\",\"潜力用户\",\"潜力用户\",\"潜力用户\",\"新用户\",\"高价值\",\"已流失\",\"已流失\",\"已流失\",\"已流失\",\"已流失\",\"已流失\",\"已流失\",\"已流失\",\"已流失\"]\n",
       "}\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"VsYWYX\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();    \n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x21235084d08>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可视化\n",
    "ggplot(rfm_df4, aes(x='rfm', fill='segment')) \\\n",
    "+ geom_bar() \\\n",
    "+ coord_flip() \\\n",
    "+ ggsize(800, 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>segment</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>低价值</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>已流失</td>\n",
       "      <td>1047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>新用户</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>易流失</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>普通用户</td>\n",
       "      <td>1135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>潜力用户</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>较低价值</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>较高价值</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>高价值</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  segment  count\n",
       "0     低价值    560\n",
       "1     已流失   1047\n",
       "2     新用户     40\n",
       "3     易流失     18\n",
       "4    普通用户   1135\n",
       "5    潜力用户    186\n",
       "6    较低价值    311\n",
       "7    较高价值    447\n",
       "8     高价值    475"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_pie = (rfm_df4[['CustomerID', 'segment']]\n",
    "                .set_index('segment')\n",
    "                .groupby(level='segment')['CustomerID']\n",
    "                .count()\n",
    "                .reset_index()\n",
    "                .rename({'CustomerID': 'count'}, axis=1)\n",
    "               )\n",
    "data_for_pie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "</script>\n",
       "\n",
       "        <div id=\"53e5c8686ab04a5abc88efb82082dfec\" style=\"width:900px; height:500px;\"></div>\n",
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       "        require(['echarts'], function(echarts) {\n",
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       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#A6CEE3\",\n",
       "        \"#1F78B4\",\n",
       "        \"#B2DF8A\",\n",
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       "        \"#FB9A99\",\n",
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       "        \"#FDBF6F\",\n",
       "        \"#FF7F00\",\n",
       "        \"#CAB2D6\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"pie\",\n",
       "            \"clockwise\": true,\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u4f4e\\u4ef7\\u503c\",\n",
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       "                {\n",
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       "                {\n",
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       "            \"radius\": [\n",
       "                \"40%\",\n",
       "                \"75%\"\n",
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       "                \"50%\"\n",
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       "                \"period\": 4\n",
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       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u4f4e\\u4ef7\\u503c\",\n",
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       "                \"\\u666e\\u901a\\u7528\\u6237\",\n",
       "                \"\\u6f5c\\u529b\\u7528\\u6237\",\n",
       "                \"\\u8f83\\u4f4e\\u4ef7\\u503c\",\n",
       "                \"\\u8f83\\u9ad8\\u4ef7\\u503c\",\n",
       "                \"\\u9ad8\\u4ef7\\u503c\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"left\": \"2%\",\n",
       "            \"top\": \"15%\",\n",
       "            \"orient\": \"vertical\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14,\n",
       "            \"textStyle\": {\n",
       "                \"color\": \"white\"\n",
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       "    \"tooltip\": {\n",
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       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
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       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
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       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Pie-Radius\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_53e5c8686ab04a5abc88efb82082dfec.setOption(option_53e5c8686ab04a5abc88efb82082dfec);\n",
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     },
     "execution_count": 48,
     "metadata": {},
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    }
   ],
   "source": [
    "(\n",
    "    Pie(init_opts=opts.InitOpts(bg_color=\"#2c343c\"))\n",
    "    .add(\n",
    "        \"\",\n",
    "        data_for_pie.values.tolist(),\n",
    "        radius=[\"40%\", \"75%\"], # 半径相对比例, 内径为40%, 外径为75%\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"Pie-Radius\"),\n",
    "        legend_opts=opts.LegendOpts(orient=\"vertical\", pos_top=\"15%\", pos_left=\"2%\", \n",
    "                                    textstyle_opts=opts.TextStyleOpts(color=\"white\") # 图例文字颜色\n",
    "                                   ),\n",
    "    )\n",
    "    .set_colors(get_map(\"Paired\", \"qualitative\", len(data_for_pie['segment'])).hex_colors)\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue; font-size:200%; font-weight:bold\">参考来源:</p>\n",
    "\n",
    "* [plot_kmeans_silhouette_analysis](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html)\n",
    "* [lets-plot.org](https://lets-plot.org/pages/api.html)\n",
    "* [joblib并行](https://joblib.readthedocs.io/en/latest/parallel.html)\n",
    "* [RFM - Customer Level Data](https://rfm.rsquaredacademy.com/articles/rfm-customer-level-data.html)\n",
    "* [RFM - Transaction Level Data](https://rfm.rsquaredacademy.com/articles/rfm-transaction-level-data.html)\n",
    "* [Customer Segmentation using RFM Analysis (R)](https://www.kaggle.com/hendraherviawan/customer-segmentation-using-rfm-analysis-r)\n",
    "* [Introduction to Customer Segmentation in Python](https://www.datacamp.com/community/tutorials/introduction-customer-segmentation-python)\n",
    "* [RFM Analysis with Python](https://www.natasshaselvaraj.com/rfm-analysis-in-python/)\n",
    "* [Create RFM Step by Step](https://www.kaggle.com/mokar2001/create-rfm-step-by-step)\n",
    "* [概念+实战讲解，一文带你了解RFM模型【kaggle项目实战分享】数据分析](https://blog.csdn.net/qq_44186838/article/details/120548206)"
   ]
  }
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